readMethods: Functions for processing files of various formats into an...
In segmentSeq: Methods for identifying small RNA loci from high-throughput sequencing data

Description Usage Arguments Details Value Author(s) See Also Examples

These functions take alignment files of various formats to produce an object (see Details) describing the alignment of sequencing tags from different libraries. At present, BAM and text files are supported.

readGeneric(files, dir = ".", replicates, libnames, chrs, chrlens, cols,
            header = TRUE, minlen = 15, maxlen = 1000,
            multireads = 1000, polyLength, estimationType = "quantile",
            discardTags = FALSE, verbose = TRUE, filterReport = NULL, ...)

readBAM(files, dir = ".", replicates, libnames, chrs, chrlens, countID = NULL,
        minlen = 15, maxlen = 1000, multireads = 1000,
        polyLength, estimationType = "quantile", discardTags = FALSE, verbose = TRUE,
        filterReport = NULL)

`files`	Filenames of the files to be read in.
`dir`	Directory (or directories) in which the files can be found.
`replicates`	A vector defining the replicate structure if the group. If and only if the ith library is a replicate of the jth library then `@replicates[i] == @replicates[j]`. This argument may be given in any form but will be stored as a factor.
`libnames`	Names of the libraries defined by the file names.
`chrs`	A chracter vector defining (a selection of) the chromosome names used in the alignment files; optional, will be inferred from data if not given.
`chrlens`	Lengths of the chromosomes to which the alignments were made; optional, will be inferred from data if not given.
`cols`	A named character vector which describes which column of the input files contains which data. See Details.
`countID`	A (two-character) string used by the BAM file to identify the ‘counts’ of individual sequenced reads; that is, how many times a given read appears in the sequenced library. If NULL, it is assumed that the data are redundant (see Details).
`header`	Do the input files have a header line? Defaults to TRUE. See Details.
`minlen`	Minimum length for reads.
`maxlen`	Maximum length for reads.
`multireads`	The functions will discard any read that aligns to the genome in more locations than given by this value. Set to Inf to keep everything. Defaults to 1000.
`polyLength`	If given, an integer value N defining the length of (approximate) homopolymers which will be removed from the data. If a tag contains a sequence of N+1 reads consisting of at least N identical bases, it will be removed. If not given, all data is used.
`estimationType`	The estimationType that will be used by the ‘baySeq’ function `getLibsizes` to infer the library sizes of the samples.
`discardTags`	If TRUE, information about the sequence of the aligned reads will be discarded. Useful for very large data sets. Defaults to FALSE.
`verbose`	Should processing information be displayed? Defaults to TRUE.
`filterReport`	If not NULL, this should be a string defining a file to which will be written those data filtered on the basis of chromsome choices, widths of sequences, multireads or polyBase.
`...`	Additional parameters to be passed to `read.table`. In particular, the ‘sep’ and ‘skip’ arguments may be useful.

readBAM: This function takes a set of BAM files and generates the 'alignmentData' object from these. If a character string for ‘countID’ is given, the function assumes the data are non-redundant and that ‘countID’ identifies the count data (i.e., how many times each read appears in the sequenced library) in each BAM file. If ‘countID’ is NULL, then it is assumed that the data are redundant, and the count data are inferred from the file.

readGeneric: The purpose of this function is to take a set of plain text files and produce an 'alignmentData' object. The function uses read.table to read in the columns of data in the files and so by default columns are separated by any white space. Alternative separators can be used by passing the appropriate value for 'sep' to read.table.

The files may contain columns with column names 'chr', 'tag', 'count', 'start', 'end', 'strand' in which case the ‘cols’ argument can be ommitted and ‘header’ set to TRUE. If this is the case, there is no requirement for all the files to have the same ordering of columns (although all must have these column names).

Alternatively, the columns of data in the input files can be specified by the ‘cols’ argument in the form of a named character vector (e.g; 'cols = c(chr = 1, tag = 2, count = 3, start = 4, end = 5, strand = 6)' would cause the function to assume that the first column contains the chromosome information, the second column contained the tag information, etc. If ‘cols’ is specified then information in the header is ignored. If ‘cols’ is missing and ‘header’ is FALSE, then it is assumed that the data takes the form described in the example above.

The 'tag', 'count' and 'strand' columns may optionally be omitted from either the file column headers or the ‘cols’ argument. If the 'tag' column is omitted, then the data will not account for duplicated sequences when estimating the number of counts in loci. If the 'count' column is omitted, the 'readGeneric' function will assume that the file contains the alignments of each copy of each sequence tag, rather than an aggregated alignment of each unique sequence. The unique alignments will be identified and the number of sequence tags aligning to each position will be calculated. If 'strand' is omitted, the strand will simply be ignored.

An alignmentData object.

Thomas J. Hardcastle

alignmentData

# Define the files containing sample information.

datadir <- system.file("extdata", package = "segmentSeq")
libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")

# Establish the library names and replicate structure.

libnames <- c("SL9", "SL10", "SL26", "SL32")
replicates <- c(1,1,2,2)

# Process the files to produce an `alignmentData' object.

alignData <- readGeneric(file = libfiles, dir = datadir, replicates =
replicates, libnames = libnames)